Overview

Dataset statistics

Number of variables12
Number of observations406572
Missing cells0
Missing cells (%)0.0%
Duplicate rows8813
Duplicate rows (%)2.2%
Total size in memory37.2 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical3

Alerts

Dataset has 8813 (2.2%) duplicate rowsDuplicates
fps_lags is highly correlated with fps_mean and 1 other fieldsHigh correlation
dropped_frames_mean is highly correlated with dropped_frames_std and 1 other fieldsHigh correlation
dropped_frames_std is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
dropped_frames_max is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
rtt_mean is highly correlated with rtt_stdHigh correlation
rtt_std is highly correlated with rtt_meanHigh correlation
fps_mean is highly correlated with fps_std and 2 other fieldsHigh correlation
fps_std is highly correlated with fps_meanHigh correlation
auto_fec_state is highly correlated with auto_fec_meanHigh correlation
auto_fec_mean is highly correlated with auto_fec_stateHigh correlation
stream_quality is highly correlated with fps_mean and 1 other fieldsHigh correlation
rtt_mean is highly skewed (γ1 = 25.23323843) Skewed
rtt_std is highly skewed (γ1 = 56.63966126) Skewed
dropped_frames_mean is highly skewed (γ1 = 31.2300008) Skewed
dropped_frames_std is highly skewed (γ1 = 73.37976309) Skewed
dropped_frames_max is highly skewed (γ1 = 29.76581419) Skewed
fps_mean has 4131 (1.0%) zeros Zeros
fps_std has 97096 (23.9%) zeros Zeros
fps_lags has 379021 (93.2%) zeros Zeros
rtt_mean has 9597 (2.4%) zeros Zeros
rtt_std has 19986 (4.9%) zeros Zeros
dropped_frames_mean has 375215 (92.3%) zeros Zeros
dropped_frames_std has 377427 (92.8%) zeros Zeros
dropped_frames_max has 375215 (92.3%) zeros Zeros
auto_fec_mean has 47276 (11.6%) zeros Zeros

Reproduction

Analysis started2022-10-05 05:12:17.536729
Analysis finished2022-10-05 05:12:32.983523
Duration15.45 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

fps_mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct682
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.49756083
Minimum0
Maximum127.1
Zeros4131
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.011747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.8
Q128.3
median30
Q343
95-th percentile57.4
Maximum127.1
Range127.1
Interquartile range (IQR)14.7

Descriptive statistics

Standard deviation11.62549412
Coefficient of variation (CV)0.3369946698
Kurtosis0.1910700531
Mean34.49756083
Median Absolute Deviation (MAD)3.5
Skewness0.5474419808
Sum14025742.3
Variance135.1521135
MonotonicityNot monotonic
2022-10-05T08:12:33.067835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3080516
 
19.8%
29.915303
 
3.8%
29.88865
 
2.2%
29.76596
 
1.6%
30.16029
 
1.5%
29.65443
 
1.3%
254773
 
1.2%
29.54686
 
1.2%
04131
 
1.0%
29.43931
 
1.0%
Other values (672)266299
65.5%
ValueCountFrequency (%)
04131
1.0%
0.18
 
< 0.1%
0.26
 
< 0.1%
0.33
 
< 0.1%
0.49
 
< 0.1%
0.55
 
< 0.1%
0.65
 
< 0.1%
0.75
 
< 0.1%
0.87
 
< 0.1%
0.93
 
< 0.1%
ValueCountFrequency (%)
127.11
< 0.1%
125.81
< 0.1%
1111
< 0.1%
102.71
< 0.1%
98.71
< 0.1%
95.41
< 0.1%
91.81
< 0.1%
83.71
< 0.1%
831
< 0.1%
80.21
< 0.1%

fps_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23910
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.285486482
Minimum0
Maximum312.5408418
Zeros97096
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.119473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.316227766
median0.994428926
Q32.59058123
95-th percentile9.503800409
Maximum312.5408418
Range312.5408418
Interquartile range (IQR)2.274353464

Descriptive statistics

Standard deviation3.708530663
Coefficient of variation (CV)1.622643884
Kurtosis385.8439851
Mean2.285486482
Median Absolute Deviation (MAD)0.994428926
Skewness7.823023193
Sum929214.8101
Variance13.75319968
MonotonicityNot monotonic
2022-10-05T08:12:33.171231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
097096
 
23.9%
0.3162277666337
 
1.6%
0.3162277664891
 
1.2%
0.3162277664091
 
1.0%
0.3162277664053
 
1.0%
0.3162277663086
 
0.8%
0.42163702142066
 
0.5%
0.42163702141975
 
0.5%
0.47140452081964
 
0.5%
0.94868329811935
 
0.5%
Other values (23900)279078
68.6%
ValueCountFrequency (%)
097096
23.9%
0.316227766202
 
< 0.1%
0.3162277664891
 
1.2%
0.3162277663086
 
0.8%
0.3162277664053
 
1.0%
0.3162277662
 
< 0.1%
0.3162277666337
 
1.6%
0.3162277664091
 
1.0%
0.316227766415
 
0.1%
0.316227766156
 
< 0.1%
ValueCountFrequency (%)
312.54084181
< 0.1%
307.16727261
< 0.1%
307.00613461
< 0.1%
151.71230081
< 0.1%
148.8989591
< 0.1%
141.15637511
< 0.1%
139.65676181
< 0.1%
103.27358491
< 0.1%
98.057182861
< 0.1%
96.997651751
< 0.1%

fps_lags
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1831508318
Minimum0
Maximum10
Zeros379021
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.220707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.099383751
Coefficient of variation (CV)6.002614019
Kurtosis68.82399684
Mean0.1831508318
Median Absolute Deviation (MAD)0
Skewness8.170234279
Sum74464
Variance1.208644631
MonotonicityNot monotonic
2022-10-05T08:12:33.265517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0379021
93.2%
119460
 
4.8%
104485
 
1.1%
22384
 
0.6%
3570
 
0.1%
4203
 
< 0.1%
5175
 
< 0.1%
697
 
< 0.1%
864
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
0379021
93.2%
119460
 
4.8%
22384
 
0.6%
3570
 
0.1%
4203
 
< 0.1%
5175
 
< 0.1%
697
 
< 0.1%
761
 
< 0.1%
864
 
< 0.1%
952
 
< 0.1%
ValueCountFrequency (%)
104485
 
1.1%
952
 
< 0.1%
864
 
< 0.1%
761
 
< 0.1%
697
 
< 0.1%
5175
 
< 0.1%
4203
 
< 0.1%
3570
 
0.1%
22384
 
0.6%
119460
4.8%

rtt_mean
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct6078
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.31439966
Minimum0
Maximum12898.4
Zeros9597
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.324705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.1
Q114.1
median32.3
Q357.1
95-th percentile182.5
Maximum12898.4
Range12898.4
Interquartile range (IQR)43

Descriptive statistics

Standard deviation133.8720623
Coefficient of variation (CV)2.464761888
Kurtosis1168.303684
Mean54.31439966
Median Absolute Deviation (MAD)19.6
Skewness25.23323843
Sum22082714.1
Variance17921.72906
MonotonicityNot monotonic
2022-10-05T08:12:33.388685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09597
 
2.4%
13.11248
 
0.3%
131206
 
0.3%
13.31198
 
0.3%
13.41188
 
0.3%
13.21182
 
0.3%
51123
 
0.3%
12.61122
 
0.3%
16.11114
 
0.3%
12.91111
 
0.3%
Other values (6068)386483
95.1%
ValueCountFrequency (%)
09597
2.4%
0.21
 
< 0.1%
0.35
 
< 0.1%
0.42
 
< 0.1%
0.56
 
< 0.1%
0.62
 
< 0.1%
0.79
 
< 0.1%
0.86
 
< 0.1%
0.93
 
< 0.1%
110
 
< 0.1%
ValueCountFrequency (%)
12898.41
 
< 0.1%
105911
 
< 0.1%
97953
< 0.1%
9021.41
 
< 0.1%
8661.11
 
< 0.1%
8360.11
 
< 0.1%
78361
 
< 0.1%
7469.21
 
< 0.1%
7191.81
 
< 0.1%
71691
 
< 0.1%

rtt_std
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct65454
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.52501865
Minimum0
Maximum40721.93329
Zeros19986
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.446307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.316227766
Q10.6992058988
median1.490711985
Q35.334374898
95-th percentile43.01107737
Maximum40721.93329
Range40721.93329
Interquartile range (IQR)4.635169

Descriptive statistics

Standard deviation156.3643372
Coefficient of variation (CV)8.008409111
Kurtosis11559.34619
Mean19.52501865
Median Absolute Deviation (MAD)1.007666093
Skewness56.63966126
Sum7938325.882
Variance24449.80596
MonotonicityNot monotonic
2022-10-05T08:12:33.499313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019986
 
4.9%
0.48304589153297
 
0.8%
0.51639777953001
 
0.7%
0.42163702142734
 
0.7%
0.3162277662671
 
0.7%
0.51639777952420
 
0.6%
0.56764621222247
 
0.6%
0.3162277662233
 
0.5%
0.67494855772176
 
0.5%
0.69920589881969
 
0.5%
Other values (65444)363838
89.5%
ValueCountFrequency (%)
019986
4.9%
0.3162277662
 
< 0.1%
0.3162277662
 
< 0.1%
0.3162277663
 
< 0.1%
0.3162277668
 
< 0.1%
0.3162277662
 
< 0.1%
0.31622776631
 
< 0.1%
0.31622776638
 
< 0.1%
0.31622776632
 
< 0.1%
0.316227766245
 
0.1%
ValueCountFrequency (%)
40721.933291
< 0.1%
6025.5310781
< 0.1%
5769.0170831
< 0.1%
5596.6950961
< 0.1%
5413.645821
< 0.1%
5215.5416931
< 0.1%
5123.8386321
< 0.1%
4986.0478331
< 0.1%
4969.8872511
< 0.1%
4931.5987941
< 0.1%

dropped_frames_mean
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct915
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1730432.424
Minimum0
Maximum2097288600
Zeros375215
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.561946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.5
Maximum2097288600
Range2097288600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation49300910.71
Coefficient of variation (CV)28.49051487
Kurtosis1023.151035
Mean1730432.424
Median Absolute Deviation (MAD)0
Skewness31.2300008
Sum7.035453715 × 1011
Variance2.430579797 × 1015
MonotonicityNot monotonic
2022-10-05T08:12:33.729185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0375215
92.3%
3.3928
 
0.2%
3.4907
 
0.2%
3.5717
 
0.2%
3.2595
 
0.1%
3.6580
 
0.1%
6498
 
0.1%
6.6428
 
0.1%
0.1420
 
0.1%
3.7417
 
0.1%
Other values (905)25867
 
6.4%
ValueCountFrequency (%)
0375215
92.3%
0.1420
 
0.1%
0.2133
 
< 0.1%
0.3108
 
< 0.1%
0.4120
 
< 0.1%
0.5105
 
< 0.1%
0.6121
 
< 0.1%
0.7120
 
< 0.1%
0.8110
 
< 0.1%
0.9115
 
< 0.1%
ValueCountFrequency (%)
20972886008
 
< 0.1%
193674600050
< 0.1%
19360914001
 
< 0.1%
17992902001
 
< 0.1%
173502310013
 
< 0.1%
168495540016
 
< 0.1%
168220120021
< 0.1%
166791120014
 
< 0.1%
166542080028
< 0.1%
16397078801
 
< 0.1%

dropped_frames_std
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct6578
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137827.8704
Minimum0
Maximum996375136.4
Zeros377427
Zeros (%)92.8%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.781396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.70042734
Maximum996375136.4
Range996375136.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9229775.537
Coefficient of variation (CV)66.96595916
Kurtosis5737.257391
Mean137827.8704
Median Absolute Deviation (MAD)0
Skewness73.37976309
Sum5.603695291 × 1010
Variance8.518875646 × 1013
MonotonicityNot monotonic
2022-10-05T08:12:33.835920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0377427
92.8%
10.43551628917
 
0.2%
11.06797181700
 
0.2%
10.75174404625
 
0.2%
10.11928851585
 
0.1%
11.38419958568
 
0.1%
0.316227766422
 
0.1%
11.70042734409
 
0.1%
18.97366596374
 
0.1%
18.65743819351
 
0.1%
Other values (6568)24194
 
6.0%
ValueCountFrequency (%)
0377427
92.8%
0.316227766422
 
0.1%
0.42163702141
 
< 0.1%
0.42163702145
 
< 0.1%
0.483045891517
 
< 0.1%
0.48304589151
 
< 0.1%
0.51639777951
 
< 0.1%
0.51639777955
 
< 0.1%
0.51639777952
 
< 0.1%
0.52704627673
 
< 0.1%
ValueCountFrequency (%)
996375136.41
< 0.1%
988728405.31
< 0.1%
935537198.31
< 0.1%
902305725.81
< 0.1%
864826861.51
< 0.1%
851095768.91
< 0.1%
838095780.21
< 0.1%
825622032.51
< 0.1%
824661111.71
< 0.1%
815553396.81
< 0.1%

dropped_frames_max
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct374
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1893338.78
Minimum0
Maximum2097288600
Zeros375215
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:33.892956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile39
Maximum2097288600
Range2097288600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52410035
Coefficient of variation (CV)27.68127688
Kurtosis922.63939
Mean1893338.78
Median Absolute Deviation (MAD)0
Skewness29.76581419
Sum7.697785346 × 1011
Variance2.746811768 × 1015
MonotonicityNot monotonic
2022-10-05T08:12:33.944541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0375215
92.3%
331187
 
0.3%
341142
 
0.3%
35858
 
0.2%
60820
 
0.2%
36752
 
0.2%
59703
 
0.2%
32693
 
0.2%
48649
 
0.2%
1602
 
0.1%
Other values (364)23951
 
5.9%
ValueCountFrequency (%)
0375215
92.3%
1602
 
0.1%
2136
 
< 0.1%
3127
 
< 0.1%
4139
 
< 0.1%
5106
 
< 0.1%
6185
 
< 0.1%
7126
 
< 0.1%
8112
 
< 0.1%
9120
 
< 0.1%
ValueCountFrequency (%)
20972886009
 
< 0.1%
193674600053
< 0.1%
19360914002
 
< 0.1%
18527962003
 
< 0.1%
17992902002
 
< 0.1%
17638382001
 
< 0.1%
173502310021
 
< 0.1%
168495540018
 
< 0.1%
168220120025
< 0.1%
166791120019
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
off
330198 
full
75964 
partial
 
410

Length

Max length7
Median length3
Mean length3.190873941
Min length3

Characters and Unicode

Total characters1297320
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoff
2nd rowoff
3rd rowoff
4th rowoff
5th rowoff

Common Values

ValueCountFrequency (%)
off330198
81.2%
full75964
 
18.7%
partial410
 
0.1%

Length

2022-10-05T08:12:33.996442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T08:12:34.045330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
off330198
81.2%
full75964
 
18.7%
partial410
 
0.1%

Most occurring characters

ValueCountFrequency (%)
f736360
56.8%
o330198
25.5%
l152338
 
11.7%
u75964
 
5.9%
a820
 
0.1%
p410
 
< 0.1%
r410
 
< 0.1%
t410
 
< 0.1%
i410
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1297320
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f736360
56.8%
o330198
25.5%
l152338
 
11.7%
u75964
 
5.9%
a820
 
0.1%
p410
 
< 0.1%
r410
 
< 0.1%
t410
 
< 0.1%
i410
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1297320
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f736360
56.8%
o330198
25.5%
l152338
 
11.7%
u75964
 
5.9%
a820
 
0.1%
p410
 
< 0.1%
r410
 
< 0.1%
t410
 
< 0.1%
i410
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1297320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f736360
56.8%
o330198
25.5%
l152338
 
11.7%
u75964
 
5.9%
a820
 
0.1%
p410
 
< 0.1%
r410
 
< 0.1%
t410
 
< 0.1%
i410
 
< 0.1%

auto_fec_state
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
partial
359296 
off
47276 

Length

Max length7
Median length7
Mean length6.534881891
Min length3

Characters and Unicode

Total characters2656900
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpartial
2nd rowpartial
3rd rowpartial
4th rowpartial
5th rowpartial

Common Values

ValueCountFrequency (%)
partial359296
88.4%
off47276
 
11.6%

Length

2022-10-05T08:12:34.087174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T08:12:34.132317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
partial359296
88.4%
off47276
 
11.6%

Most occurring characters

ValueCountFrequency (%)
a718592
27.0%
p359296
13.5%
r359296
13.5%
t359296
13.5%
i359296
13.5%
l359296
13.5%
f94552
 
3.6%
o47276
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2656900
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a718592
27.0%
p359296
13.5%
r359296
13.5%
t359296
13.5%
i359296
13.5%
l359296
13.5%
f94552
 
3.6%
o47276
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2656900
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a718592
27.0%
p359296
13.5%
r359296
13.5%
t359296
13.5%
i359296
13.5%
l359296
13.5%
f94552
 
3.6%
o47276
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2656900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a718592
27.0%
p359296
13.5%
r359296
13.5%
t359296
13.5%
i359296
13.5%
l359296
13.5%
f94552
 
3.6%
o47276
 
1.8%

auto_fec_mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct118
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.41353561
Minimum0
Maximum250
Zeros47276
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-10-05T08:12:34.172703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median50
Q350
95-th percentile100
Maximum250
Range250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.83604485
Coefficient of variation (CV)0.6775656339
Kurtosis9.141770929
Mean51.41353561
Median Absolute Deviation (MAD)0
Skewness2.425718249
Sum20903304
Variance1213.550021
MonotonicityNot monotonic
2022-10-05T08:12:34.223584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50312482
76.9%
047276
 
11.6%
10021152
 
5.2%
20013841
 
3.4%
206955
 
1.7%
402756
 
0.7%
60246
 
0.1%
22159
 
< 0.1%
42133
 
< 0.1%
4482
 
< 0.1%
Other values (108)1490
 
0.4%
ValueCountFrequency (%)
047276
11.6%
42
 
< 0.1%
519
 
< 0.1%
61
 
< 0.1%
85
 
< 0.1%
1026
 
< 0.1%
122
 
< 0.1%
141
 
< 0.1%
1517
 
< 0.1%
206955
 
1.7%
ValueCountFrequency (%)
25013
 
< 0.1%
2351
 
< 0.1%
2301
 
< 0.1%
2201
 
< 0.1%
2101
 
< 0.1%
20013841
3.4%
1952
 
< 0.1%
19014
 
< 0.1%
18524
 
< 0.1%
1822
 
< 0.1%

stream_quality
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
0
378738 
1
 
27834

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters406572
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0378738
93.2%
127834
 
6.8%

Length

2022-10-05T08:12:34.271348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T08:12:34.313545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0378738
93.2%
127834
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0378738
93.2%
127834
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number406572
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0378738
93.2%
127834
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common406572
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0378738
93.2%
127834
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII406572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0378738
93.2%
127834
 
6.8%

Interactions

2022-10-05T08:12:31.486285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:24.879323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.697388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.511628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.452647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.215516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.021167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.773557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.575189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.563147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:24.981388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.782923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.602597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.532306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.303331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.107107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.865001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.660903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.643173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.062012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.869717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.692758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.617467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.390687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.188998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.955303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.887220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.722694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.142106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.959522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.785886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.703029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.483964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.278103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.067640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.967081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.805331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.241330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.052718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.880086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.783062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.569105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.359942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.156824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.052359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.892979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.329327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.147133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.971804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.873207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.662215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.442102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.244978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.139855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.976762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.418457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.234368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.062761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.961875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.751575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.527320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.324470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.224859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:32.059386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.509827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.327317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.151627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.050717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.842617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.611635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.406386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.311118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:32.140748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:25.600133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:26.417510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:27.233109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.134221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:28.931242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:29.692600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:30.485002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-05T08:12:31.399981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-05T08:12:34.346998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-05T08:12:34.417095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-05T08:12:34.491011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-05T08:12:34.559893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-05T08:12:34.614108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-05T08:12:32.276776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-05T08:12:32.574441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality
024.40.516398091.16.7239210.00.00.0offpartial50.00
128.62.065591099.715.9237770.00.00.0offpartial50.00
230.00.000000098.111.7987760.00.00.0offpartial50.00
330.30.948683099.413.0145220.00.00.0offpartial50.00
429.90.3162280123.262.4763070.00.00.0offpartial50.00
529.51.6499160131.2114.2577980.00.00.0offpartial50.00
624.30.483046098.316.4994950.00.00.0offpartial50.00
724.50.9718250141.9103.8144180.00.00.0offpartial50.00
830.00.0000000107.518.7335110.00.00.0offpartial50.00
930.00.4714050108.210.9524220.00.00.0offpartial50.00

Last rows

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality
40656240.00.000.00.00.00.00.0fullpartial50.00
40656340.00.000.00.00.00.00.0fullpartial50.00
40656440.00.000.00.00.00.00.0fullpartial50.00
40656540.00.000.00.00.00.00.0fullpartial50.00
40656640.00.000.00.00.00.00.0fullpartial50.00
40656740.00.000.00.00.00.00.0fullpartial50.00
40656840.00.000.00.00.00.00.0fullpartial50.00
40656940.00.000.00.00.00.00.0fullpartial50.00
40657040.00.000.00.00.00.00.0fullpartial50.00
40657140.00.000.00.00.00.00.0fullpartial50.00

Duplicate rows

Most frequently occurring

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality# duplicates
20.00.0100.00.00.00.00.0offoff0.011787
136030.00.000.00.00.00.00.0offpartial50.00763
46225.00.000.00.00.00.00.0offpartial50.00290
78528.00.000.00.00.00.00.0offpartial50.00251
560.00.0100.00.07471184.00.07471184.0offoff0.01248
82929.00.000.00.00.00.00.0offpartial50.00242
869057.00.0057.00.00.00.00.0offpartial50.00240
19524.00.000.00.00.00.00.0offpartial50.00230
71326.00.000.00.00.00.00.0offpartial50.00203
880360.00.0086.00.00.00.00.0offpartial50.00202